CN111161241B

CN111161241B - Liver image identification method, electronic equipment and storage medium

Info

Publication number: CN111161241B
Application number: CN201911381945.1A
Authority: CN
Inventors: 刘莉; 田疆; 钟诚
Original assignee: Lenovo Beijing Ltd
Current assignee: Lenovo Beijing Ltd
Priority date: 2019-12-27
Filing date: 2019-12-27
Publication date: 2024-04-23
Anticipated expiration: 2039-12-27
Also published as: CN111161241A

Abstract

The embodiment of the application discloses a liver image identification method, which comprises the following steps: acquiring a medical image sequence of a liver to be segmented; determining a corresponding liver region mask image sequence and a liver vessel mask image sequence based on the medical image sequence; determining a portal vein image sequence and a hepatic vein image sequence based on the liver region mask image sequence and the liver vessel mask image sequence; dividing the medical image sequence, the liver region mask image sequence and the liver vessel mask image sequence by adopting a target classifier to determine a liver tail leaf image sequence; removing a liver tail leaf image sequence from the liver region mask image sequence to obtain a target liver region mask image sequence; and dividing the mask image sequence of the target liver region based on the hepatic vein image sequence and the portal vein image sequence to obtain a target liver segment. The embodiment of the application also provides electronic equipment and a storage medium.

Description

Liver image identification method, electronic equipment and storage medium

Technical Field

The present application relates to the field of image processing technologies, and in particular, to a liver image recognition method, an electronic device, and a storage medium.

Background

With the rapid development of computer aided diagnosis technology and computer graphic image processing technology, the computer liver aided diagnosis system is applied to the primary screening of liver tumors and liver excision surgery, thereby achieving the purpose of assisting doctors. At present, a common computer liver auxiliary diagnosis system generally performs corresponding processing on magnetic resonance imaging (Magnetic Resonance Imaging, MRI) or electronic computer tomography (Computed Tomography, CT) images to divide liver regions and intrahepatic blood vessels, then segments the liver regions based on the characteristics of the intrahepatic blood vessels, and performs corresponding three-dimensional display on segmented liver parts to realize the segmentation of the liver.

However, in the method for segmenting the liver, the doctor is also dependent on the marking of certain liver system parts, so that the liver parts cannot be automatically segmented, the intelligent degree of the electronic equipment is low, and the accuracy of the electronic equipment for segmenting the liver parts is low.

Content of the application

In order to solve the technical problems, the embodiment of the application expects to provide a liver image recognition method, electronic equipment and storage medium, solves the problem that the liver part cannot be fully automatically segmented in the prior art, and improves the accuracy of segmenting the liver part by the electronic equipment and the intelligent degree of the electronic equipment.

The technical scheme of the application is realized as follows:

In a first aspect, a liver image recognition method, the method comprising:

acquiring a medical image sequence of a liver to be segmented; the medical image sequence of the liver to be segmented is used for forming a complete three-dimensional liver shape of the liver to be segmented;

Determining a corresponding liver region mask image sequence and a liver vessel mask image sequence based on the medical image sequence;

determining a portal vein image sequence and a hepatic vein image sequence based on the liver region mask image sequence and the liver vessel mask image sequence;

Dividing the medical image sequence, the liver region mask image sequence and the liver blood vessel mask image sequence by adopting a target classifier to determine a liver tail leaf image sequence;

Removing the liver tail leaf image sequence from the liver region mask image sequence to obtain a target liver region mask image sequence;

And dividing the target liver region mask image sequence based on the hepatic vein image sequence and the portal vein image sequence to obtain a target liver segment.

Optionally, the determining a portal vein image sequence and a hepatic vein image sequence based on the liver region mask image sequence and the liver vessel mask image sequence includes:

determining a inferior vena cava image sequence based on the liver region mask image sequence and the liver vessel mask image sequence;

removing the inferior vena cava in the liver vessel mask image sequence based on the inferior vena cava image sequence to obtain a first vessel mask image sequence;

and carrying out refinement treatment on blood vessels in the first vascular mask image sequence, and determining the portal vein image sequence and the hepatic vein image sequence.

Optionally, the thinning processing is performed on the blood vessels in the first blood vessel mask image sequence, and determining the portal vein image sequence and the hepatic vein image sequence includes:

carrying out refinement treatment on blood vessels in the first blood vessel mask image sequence to obtain a second blood vessel mask image sequence;

determining a first position range of the portal vein and a second position range of the hepatic vein;

Acquiring a blood vessel image sequence in the first position range from the second blood vessel mask image sequence to obtain the portal vein image sequence;

And acquiring a blood vessel image sequence in the second position range from the second blood vessel mask image sequence to obtain the hepatic vein image sequence.

Optionally, the segmenting the target liver region mask image sequence based on the hepatic vein image sequence and the portal vein image sequence to obtain a target liver segment includes:

Determining a first segmentation line for segmenting the hepatic vein image sequence;

dividing the target liver region mask image sequence based on the first dividing line to obtain a first target segment;

segmenting the portal vein image sequence to obtain a target portal vein segment;

And dividing the first target segment based on the target portal vein segment to obtain the target liver segment.

Optionally, the determining, based on the hepatic vein image sequence, a first segmentation line for segmenting the hepatic vein image sequence includes:

Determining a medical image type of the medical image sequence and determining a vessel trend based on the medical image type;

Based on the blood vessel trend, carrying out region communication analysis on the hepatic vein image sequence to obtain a hepatic left vein communication region, a hepatic middle vein communication region and a hepatic right vein communication region in the hepatic vein image sequence;

projecting the hepatic vein image sequence to a preset plane to obtain a hepatic vein projection image comprising hepatic vein distribution;

Determining a first region where a hepatic left vein branch corresponding to the hepatic left vein communication region is located, a second region where a hepatic middle vein branch corresponding to the hepatic middle vein communication region is located and a third region where a hepatic right vein branch corresponding to the hepatic right vein communication region is located in the hepatic vein projection image;

the first split line is determined based on the first region, the second region, and the third region.

Optionally, the determining the first dividing line based on the first region, the second region and the third region includes:

performing linear fitting on the coordinate position of each pixel point in the first region to obtain a first sub-dividing line in the first dividing line;

Performing linear fitting on the coordinate position of each pixel point in the second region to obtain a second sub-dividing line in the first dividing line;

And performing linear fitting on the coordinate position of each pixel point in the third region to obtain a third sub-dividing line in the first dividing line.

Optionally, the segmenting the target liver region mask image sequence based on the first segmentation line to obtain a first target segment includes:

Dividing the mask image sequence of the target liver region by adopting the first sub-dividing line, the second sub-dividing line and the third sub-dividing line, and sequentially determining the divided regions into a left outer region image sequence, a left inner region image sequence, a right front region image sequence and a right rear region image sequence of the liver to be segmented according to a preset coordinate direction; wherein the first target segment comprises the left outer region image sequence, the left inner region image sequence, the right front region image sequence, and the right rear region image sequence.

Optionally, the segmenting the portal vein image sequence to obtain a target portal vein segment includes:

Constructing a portal vein tree based on the portal vein image sequence;

determining three or more branches in the portal vessel tree as target branches;

And analyzing and processing the target branch, and determining an upper section and a lower section of the left hepatic portal vein of the liver to be segmented and an upper section and a lower section of the right hepatic portal vein of the liver to be segmented in the portal vein image sequence.

Optionally, the segmenting the first target segment based on the target portal vein segment to obtain the target liver segment includes:

Dividing the left outer region image sequence based on the upper and lower left hepatic portal vein image sequences to obtain an upper left outer region image sequence and a lower left outer region image sequence in the target liver segment;

Dividing the right anterior region image sequence and the right posterior region image sequence in sequence based on the upper and lower right hepatic portal vein image sequences to obtain a right anterior region upper segment image sequence, a right anterior region lower segment image sequence, a right posterior region upper segment image sequence and a right posterior region lower segment image sequence in the target liver segment; wherein the target liver segment further comprises the left intraregion image sequence.

Optionally, the segmenting the left outer region image sequence based on the left hepatic portal vein upper and lower segment image sequences to obtain a left outer region upper segment image sequence and a left outer region lower segment image sequence in the target liver segment includes:

Acquiring a first coordinate position of each pixel of each image in the left outer region image sequence;

Acquiring a second coordinate position of each pixel of each image in the upper segment image sequence of the left hepatic portal vein; the images in the left outer region image sequence and the images in the left portal vein upper segment image sequence have a first corresponding relation;

Acquiring a third coordinate position of each pixel of each image in the left hepatic portal subimage sequence; the images in the left outer region image sequence and the images in the left portal vein hypomere image sequence have a second corresponding relation;

Calculating the distance between the first coordinate position and the second coordinate position based on the first corresponding relation to obtain a first distance set;

calculating the distance between the first coordinate position and the third coordinate position based on the second corresponding relation to obtain a second distance set;

And classifying the coordinate position of each pixel of each image in the left outer region image sequence by adopting a preset classification algorithm to the first distance set and the second distance set to obtain the left outer region upper segment image sequence and the left outer region lower segment image sequence.

Optionally, the method further includes, after the segmenting the liver region mask image sequence after removing the liver tail leaf based on the liver tail leaf image sequence based on the hepatic vein image sequence and the portal vein image sequence to obtain the target liver segment:

Marking the liver tail leaf image sequence as 1 segment;

And marking the left outer region upper section image sequence as 2 sections, the left outer region lower section image sequence as 3 sections, the left inner region image sequence as 4 sections, the right front region upper section image sequence as 5 sections, the right rear region upper section image sequence as 6 sections, the right rear region lower section image sequence as 7 sections and the right front region lower section image sequence as 8 sections.

In a second aspect, an electronic device, the electronic device comprising: a processor, a memory, and a communication bus, wherein:

the memory is used for storing executable instructions;

the communication bus is used for realizing communication connection between the processor and the memory;

The processor is configured to execute the liver image recognition program stored in the memory, and implement the liver image recognition method according to any one of the above.

In a third aspect, a storage medium has stored thereon a liver image recognition program which, when executed by a processor, implements the steps of the liver image recognition method as set forth in any one of the preceding claims.

The embodiment of the application provides a liver image recognition method, electronic equipment and a storage medium, wherein after a medical image sequence of a liver to be segmented is acquired, a corresponding liver region mask image sequence and a corresponding liver blood vessel mask image sequence are determined based on the medical image sequence, then a portal vein image sequence and a hepatic vein image sequence are determined based on the liver region mask image sequence and the liver blood vessel mask image sequence, and after the portal vein image sequence and the hepatic vein image sequence are determined, a target classifier is adopted to segment the medical image sequence, the liver region mask image sequence and the liver blood vessel mask image sequence, a liver tail-shaped leaf image sequence is determined, the liver tail-shaped leaf image sequence is removed from the liver region mask image sequence, a target liver region mask image sequence is obtained, and finally the target liver region mask image sequence is segmented based on the hepatic vein image sequence and the portal vein image sequence, so that target liver segmentation is obtained. In this way, the medical image sequence, the liver region mask image sequence and the liver blood vessel mask image sequence are segmented, after the liver tail-shaped leaf image sequence is determined, the target liver region mask image sequence of the liver region mask image sequence after the liver tail-shaped leaf image sequence is removed, and the target liver region mask image sequence is segmented through the portal vein image sequence and the liver vein image sequence to obtain a target segment, so that the segmentation of the liver tail-shaped leaf image sequence is realized, the segmentation of the target liver region mask image sequence based on the portal vein image sequence and the liver vein image sequence is realized, the problem that the liver part cannot be segmented fully automatically in the prior art is solved, and the accuracy of segmenting the liver part by the electronic equipment and the intelligent degree of the electronic equipment are improved.

Drawings

Fig. 1 is a schematic flow chart of a liver image recognition method according to an embodiment of the present application;

Fig. 2 is a flow chart of another liver image recognition method according to an embodiment of the present application;

FIG. 3 is a schematic view of a liver medical image according to an embodiment of the present application;

fig. 4 is a schematic diagram of a mask image of a liver region according to an embodiment of the present application;

FIG. 5 is a schematic diagram of a mask image of a liver blood vessel according to an embodiment of the present application;

Fig. 6 is a flowchart of another liver image recognition method according to an embodiment of the present application;

fig. 7 is a schematic diagram of an application scenario provided in an embodiment of the present application;

fig. 8 is a schematic diagram of another application scenario provided in an embodiment of the present application;

fig. 9 is a flowchart of another liver image recognition method according to an embodiment of the present application;

fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application.

An embodiment of the present application provides a liver image recognition method, referring to fig. 1, the method is applied to an electronic device, and the method includes the following steps:

Step 101, acquiring a medical image sequence of the liver to be segmented.

Wherein the sequence of medical images of the liver to be segmented is used to construct a complete three-dimensional liver shape of the liver to be segmented.

In the embodiment of the application, the medical image sequence of the liver to be segmented can be a cross-sectional image sequence of the liver to be segmented, a sagittal image sequence, a coronal image sequence, and the acquisition mode of acquiring the medical image sequence can be magnetic resonance imaging (Magnetic Resonance Imaging, MRI) or electronic computed tomography (Computed Tomography, CT). The CT uses precisely collimated X-ray beams, gamma rays, ultrasonic waves and the like to scan the cross section of the liver part of the human body around the detector with extremely high sensitivity, has the characteristics of quick scanning time, clear images and the like, and can be used for checking various diseases; the rays used can be classified differently according to the type: x-ray CT (X-CT), gamma-ray CT (gamma-CT), and the like. The electronic device may be a device such as a computer that can process an image.

Step 102, determining a corresponding liver region mask image sequence and a corresponding liver vessel mask image sequence based on the medical image sequence.

In the embodiment of the application, after the medical image sequence is obtained, since the medical image sequence has the image content of other body tissues except liver tissues, in order to improve the segmentation accuracy of the liver to be segmented, the image of the liver tissue in the obtained medical image sequence needs to be extracted and identified to obtain the image of the region of the dirty, namely the mask image sequence of the liver region. In order to further improve the accuracy of segmenting the liver to be segmented, blood vessels related to the liver in the medical image sequence are extracted and identified, and a liver blood vessel mask image sequence is obtained. Wherein the vessels in the liver vessel mask image sequence include all intrahepatic vessels and part of extrahepatic vessels, part of extrahepatic vessels such as inferior vena cava.

Step 103, determining a portal vein image sequence and a hepatic vein image sequence based on the hepatic region mask image sequence and the hepatic vessel mask image sequence.

In the embodiment of the application, since the portal vein of the liver blood vessel and the hepatic vein have obvious distinguishing characteristics, the mask image sequence of the liver region and the mask image sequence of the liver blood vessel can be processed to a certain degree according to the characteristics of the portal vein and the hepatic vein, and the portal vein image sequence and the hepatic vein image sequence are distinguished.

And 104, segmenting the medical image sequence, the liver region mask image sequence and the liver blood vessel mask image sequence by adopting a target classifier to determine a liver tail leaf image sequence.

In the embodiment of the application, the target classifier is an image classifier which is obtained by training a large amount of liver tail leaf images, namely a trained neural network model, and can segment the liver tail leaf images in the liver with high precision.

And 105, removing the liver tail leaf image sequence from the liver region mask image sequence to obtain a target liver region mask image sequence.

In the embodiment of the application, since the liver tail leaf image sequence with high precision is obtained, the liver tail leaf region in the image sequence corresponding to the liver tail leaf image sequence in the liver region mask image sequence can be removed, for example, the pixel value of the liver tail leaf region part is set as the pixel value of the background pixel in the liver region mask image sequence, so that the target liver region mask image sequence is obtained.

And 106, dividing the mask image sequence of the target liver region based on the hepatic vein image sequence and the portal vein image sequence to obtain a target liver segment.

In the embodiment of the application, a hepatic vein image sequence and a portal vein image sequence are adopted, namely, a target liver region mask image sequence is accurately segmented based on hepatic veins and portal veins, so that the segmentation of the liver to be segmented is realized, and 8 segments including a liver tail leaf segment corresponding to the liver tail leaf image sequence are obtained.

The embodiment of the application provides a liver image recognition method, which comprises the steps of acquiring a medical image sequence of a liver to be segmented, determining a corresponding liver region mask image sequence and a liver blood vessel mask image sequence based on the medical image sequence, determining a portal vein image sequence and a hepatic vein image sequence based on the liver region mask image sequence and the liver blood vessel mask image sequence, segmenting the medical image sequence, the liver region mask image sequence and the liver blood vessel mask image sequence by using a target classifier, determining a liver tail-shaped leaf image sequence, removing the liver tail-shaped leaf image sequence from the liver region mask image sequence to obtain a target liver region mask image sequence, and finally segmenting the target liver region mask image sequence based on the hepatic vein image sequence and the portal vein image sequence to obtain a target liver segment. In this way, the medical image sequence, the liver region mask image sequence and the liver blood vessel mask image sequence are segmented, after the liver tail-shaped leaf image sequence is determined, the target liver region mask image sequence of the liver region mask image sequence after the liver tail-shaped leaf image sequence is removed, and the target liver region mask image sequence is segmented through the portal vein image sequence and the liver vein image sequence to obtain a target segment, so that the segmentation of the liver tail-shaped leaf image sequence is realized, the segmentation of the target liver region mask image sequence based on the portal vein image sequence and the liver vein image sequence is realized, the problem that the liver part cannot be segmented fully automatically in the prior art is solved, and the accuracy of segmenting the liver part by the electronic equipment and the intelligent degree of the electronic equipment are improved.

Based on the foregoing embodiments, an embodiment of the present application provides a liver image recognition method, referring to fig. 2, the method is applied to an electronic device, and the method includes the following steps:

step 201, acquiring a medical image sequence of a liver to be segmented.

In the embodiment of the present application, for example, CT is used to sample abdominal cavities of a patient at intervals to obtain CT image sequences of a plurality of positions of the liver, and assuming that the size of each CT image is 512×512 and 128 pieces of the CT images are sampled altogether, the medical image sequence of the liver to be segmented is 512×512×128 pieces of the CT images, wherein the 128 pieces of CT images are sequentially arranged according to the liver position order, that is, when the 128 pieces of CT images are superimposed according to the arrangement order, the corresponding liver portion can obtain a three-dimensional shape of the liver. Wherein a CT image of a sequence of medical images may be as shown in fig. 3.

Step 202, determining a corresponding liver region mask image sequence and a corresponding liver vessel mask image sequence based on the medical image sequence.

In the embodiment of the application, the liver region mask image sequence can be obtained by segmenting the medical image sequence by adopting a neural network model algorithm. The liver vessel mask image sequence may be obtained using, for example, a convolutional neural network model or a mesh-based split line optimization algorithm. When a liver region mask image sequence and a liver blood vessel mask image sequence are obtained, setting the pixel value of the liver in each image of the medical image sequence as 1, setting the corresponding pixels except the liver as a background, and setting the corresponding background pixel as 0, so that the liver region mask image sequence can be obtained; setting the pixel value of the blood vessel in the liver region in each image of the medical image sequence to be 1, setting the corresponding pixels except the blood vessel to be the background, and setting the corresponding background pixel to be 0, thus obtaining the liver blood vessel mask image sequence. In other application scenarios, the pixel value of the liver region in the liver region mask image sequence may also be set to 0, the corresponding background pixel value is set to 1, the pixel value of the blood vessel in the liver blood vessel mask image sequence is set to 0, and the corresponding background pixel value is set to 1. Namely, the method can be adopted as long as the liver or liver blood vessel can be distinguished and identified. FIG. 4 is a schematic view of a liver mask image obtained by processing the medical image of FIG. 3; fig. 5 is a schematic view of a mask image of a liver vessel obtained by processing the medical image shown in fig. 3.

Step 203, determining a portal vein image sequence and a hepatic vein image sequence based on the hepatic region mask image sequence and the hepatic vessel mask image sequence.

In the embodiment of the application, the number of images of the portal vein image sequence is generally lower than that of the liver region mask image sequence, and the number of images of the portal vein image sequence is generally lower than that of the liver blood vessel mask image sequence; the number of images of the hepatic vein image sequence is generally lower than the number of images of the hepatic region mask image sequence, and the number of images of the hepatic vein image sequence is also generally lower than the number of images of the hepatic vessel mask image sequence.

In other embodiments of the present application, step 203 may be implemented by the following steps 203 a-203 c:

Step 203a, determining a inferior vena cava image sequence based on the liver region mask image sequence and the liver vessel mask image sequence.

In the embodiment of the application, since the inferior vena cava is a long blood vessel extending longitudinally outside the liver, i.e., in a direction parallel to the spine of the patient, the liver region mask image sequence can be used to process the liver blood vessel mask image sequence. Assuming that the pixel value of the liver region in the liver region mask image sequence is 0, the background pixel value is 1, the pixel value of the blood vessel region in the liver blood vessel mask image sequence is 0, the background pixel value is 1, and correspondingly, the processing of the liver blood vessel mask image sequence by using the liver region mask image sequence specifically can be as follows: assuming that the coordinates of the medical image sequence are three-dimensional coordinates (x, y, z) respectively, the cross section of the liver is on the xy plane, the x-axis is the horizontal direction vertical to the spine of the patient, the y-axis is the horizontal direction parallel to the back of the patient, the z-axis is the direction parallel to the spine, the lower end of the corresponding liver is the region close to the tail spine of the patient, the upper end of the liver is the region close to the cervical vertebra of the patient, the ith serial number liver vessel mask image and the ith serial number liver region mask image are acquired from the liver vessel mask image sequence and the liver region mask image sequence respectively, in the ith serial number liver vessel mask image, the target pixel point corresponding to the pixel point with the pixel value of 0 in the ith serial number liver region mask image is determined, and the pixel value of the target pixel point is set to be 1, wherein the value of i is from 1 to 128, so that n vessel images with only one connected region can be obtained, and the n vessel images can be determined to belong to the lower vena region at the upper end of the liver. After the cross section of the inferior vena cava region at the upper end of n pieces of liver is found, assuming that n is gradually increased from 1 in the direction from the upper end of the liver to the lower end of the liver in the z-back direction, determining the vascular region closest to the vascular region in the same pixel region in the nth piece of blood vessel image from the n+1th piece of blood vessel image according to the vascular region in the determined nth piece of blood vessel image, carrying out corresponding identification, and determining the vascular region as the inferior vena cava, so that the process is repeated until the 128 th piece of image is found, and combining all the inferior vena cava regions to obtain the whole inferior vena cava blood vessel.

Step 203b, removing the inferior vena cava in the liver vessel mask image sequence based on the inferior vena cava image sequence, and obtaining a first vessel mask image sequence.

In the embodiment of the application, in the liver vascular mask image sequence, the inferior vena cava image corresponding to each liver vascular mask image is determined from the inferior vena cava image sequence, then the pixel value of the pixel point corresponding to the vascular region in the inferior vena cava image in each liver vascular mask image is set as the background pixel value, so that the inferior vena cava in the liver vascular mask image sequence can be removed, the interference of the inferior vena cava on the portal vein and the hepatic vein vascular region is eliminated, and the accuracy of the subsequent segmentation of the liver to be segmented is improved.

And 203c, carrying out thinning treatment on blood vessels in the first blood vessel mask image sequence, and determining a portal vein image sequence and a hepatic vein image sequence.

In the embodiment of the application, the portal vein and the hepatic vein are respectively converged at the lower end and the upper end of the liver, and when the liver is segmented, only the hepatic vein and the main trunk area of the portal vein are considered, so that each image in the first vascular mask image sequence can be subjected to refinement treatment by adopting a refinement treatment method such as a topology refinement method (Topological Thinning), and fine branches of blood vessels are removed. Depending on the portal vein and hepatic vein specificity, it may be determined that the portal vein is always located in a single communication region at the lower end of the liver, and the refined hepatic vein is located in a plurality of communication regions at the upper end of the liver. Here, the communication region is a communication in the z-axis direction.

And 204, segmenting the medical image sequence, the liver region mask image sequence and the liver blood vessel mask image sequence by adopting a target classifier to determine a liver tail leaf image sequence.

In the embodiment of the application, the tail lobe of the liver is characterized by being semi-circularly wrapped around the inferior vena cava behind the liver and being positioned between the hepatic vein and the portal vein in the liver. In some application scenarios, the segmentation of the hepatic tail lobe may be performed only by using the liver region mask image sequence and the liver vessel mask image sequence, or the medical image sequence, but generally the segmentation accuracy is lower.

And 205, removing the liver tail leaf image sequence from the liver region mask image sequence to obtain a target liver region mask image sequence.

In the embodiment of the application, the pixel value of the pixel point where the liver tail leaf corresponding to the liver tail leaf image sequence is located in each image of the liver region mask image sequence is set as the background pixel, so that the process of removing the liver tail leaf can be realized, and the target liver region mask image sequence which does not comprise the liver tail leaf is obtained.

And 206, dividing the mask image sequence of the target liver region based on the hepatic vein image sequence and the portal vein image sequence to obtain a target liver segment.

In other embodiments of the present application, step 206 may be implemented by the following steps 206 a-206 d:

step 206a, determining a first segmentation line for segmenting the hepatic vein image sequence.

In the embodiment of the application, according to the characteristics of the hepatic vein, namely, three branches of the hepatic left vein, the hepatic right vein and the hepatic middle vein exist in the hepatic vein, the first dividing line can be determined according to the three branches of the hepatic left vein, the hepatic right vein and the hepatic middle vein, and the corresponding first dividing line can be a set of dividing lines according to the first dividing line, and at least comprises three sub dividing lines.

And 206b, dividing the mask image sequence of the target liver region based on the first dividing line to obtain a first target segment.

In the embodiment of the application, each image in the mask image sequence of the target liver region is divided into at least three parts by the first dividing line, so that a first target segment can be obtained.

Step 206c, segmenting the portal vein image sequence to obtain a target portal vein segment.

In the embodiment of the application, based on the characteristics of portal veins, portal vein topological structure analysis is performed on a portal vein image sequence in the z-axis direction, a portal vein tree is established, and the portal veins can be divided into target portal vein segments, including four segments of a right upper liver end, a right lower liver end, a left upper liver end and a left lower liver end.

Step 206d, dividing the first target segment based on the target portal vein segment to obtain a target liver segment.

In the embodiment of the application, the target portal vein segment performs upper and lower segment segmentation on the first target segment divided into at least three segments, so as to obtain other seven segments except for the tail-shaped lobe segment of the liver, namely the target liver segment.

It should be noted that, in this embodiment, the descriptions of the same steps and the same content as those in other embodiments may refer to the descriptions in other embodiments, and are not repeated here.

Based on the foregoing embodiments, an embodiment of the present application provides a liver image recognition method, referring to fig. 6, the method is applied to an electronic device, and the method includes the steps of:

step 301, acquiring a medical image sequence of a liver to be segmented.

Step 302, determining a corresponding liver region mask image sequence and a liver vessel mask image sequence based on the medical image sequence.

Step 303, determining a inferior vena cava image sequence based on the liver region mask image sequence and the liver vessel mask image sequence.

Step 304, removing the inferior vena cava in the liver vessel mask image sequence based on the inferior vena cava image sequence to obtain a first vessel mask image sequence.

And 305, carrying out thinning treatment on blood vessels in the first blood vessel mask image sequence, and determining a portal vein image sequence and a hepatic vein image sequence.

In other embodiments of the present application, step 305 may be implemented by the following steps 305 a-305 d:

and 305a, carrying out thinning treatment on blood vessels in the first blood vessel mask image sequence to obtain a second blood vessel mask image sequence.

In the embodiment of the application, in the second vascular mask image sequence, one connected region exists in some images, and a plurality of connected regions exist in some images.

Step 305b, determining a first location range of the portal vein and a second location range of the hepatic vein.

In the embodiment of the application, the first position range and the second position range are determined according to the characteristics of the portal vein and the hepatic vein, namely, the portal vein is always at the lower end of the liver, the hepatic vein is always at the upper end of the liver, the portal vein is usually a single communication area, and a plurality of communication areas exist in the hepatic vein.

And 305c, acquiring a blood vessel image sequence in the first position range from the second blood vessel mask image sequence to obtain a portal vein image sequence.

In an embodiment of the present application, the first position range is in the z-axis direction and is in the upper end range of the liver.

And 305d, acquiring a blood vessel image sequence in a second position range from the second blood vessel mask image sequence to obtain a hepatic vein image sequence.

In an embodiment of the present application, the second position range is in the z-axis direction, at the lower end of the liver.

And 306, segmenting the medical image sequence, the liver region mask image sequence and the liver blood vessel mask image sequence by adopting a target classifier to determine a liver tail leaf image sequence.

And 307, removing the liver tail leaf image sequence from the liver region mask image sequence to obtain a target liver region mask image sequence.

Step 308, determining a first segmentation line for segmenting the hepatic vein image sequence.

In other embodiments of the present application, step 308 may be implemented by the steps of:

step 308a, determining a medical image type of the medical image sequence and determining a vessel trend based on the medical image type.

In an embodiment of the application, the medical image types include sagittal images, coronal images, and transverse images. In a cross-sectional image, the course of a blood vessel may be defined, for example, from the patient's head to the foot; in the sagittal image, the vessel trend may be defined as going from the left hand direction to the right hand direction of the patient; in the coronal image, the vessel trend may be defined as the direction from the patient's chest to the back.

And 308b, carrying out region communication analysis on the hepatic vein image sequence based on the blood vessel trend to obtain a hepatic left vein communication region, a hepatic middle vein communication region and a hepatic right vein communication region in the hepatic vein image sequence.

In the embodiment of the present application, the above-described embodiment is described taking the case where the determined type of medical image is a cross-sectional image, that is, in the z-axis direction, the region where the pixel region of the blood vessel region in the adjacent image in the hepatic vein image sequence is closest to the nearest region is determined to be the region connection, so that it is possible to determine which of the hepatic left vein connection region, the hepatic middle vein connection region, and the hepatic right vein connection region the blood vessel region in each hepatic vein image in the hepatic vein image sequence belongs to, and it is possible to identify the connection regions belonging to the same category using different identification information.

Step 308c, projecting the hepatic vein image sequence to a preset plane to obtain a hepatic vein projection image including hepatic vein distribution.

In the embodiment of the present application, the preset plane is generally a plane parallel to the patient's spine, that is, a plane perpendicular to the patient's spine when the hepatic vein image sequence is projected regardless of the type of medical image. Illustratively, the hepatic vein image sequence is projected onto a plane parallel to the patient's spine, the yz plane, where the hepatic vein region in the jth hepatic vein image in the hepatic vein image sequence is marked, illustratively.

Step 308d, determining a first region where a hepatic left vein branch corresponding to the hepatic left vein communication region is located, a second region where a hepatic middle vein branch corresponding to the hepatic middle vein communication region is located, and a third region where a hepatic right vein branch corresponding to the hepatic right vein communication region is located in the hepatic vein projection image.

In some application scenarios, the three-dimensional hepatic vein distribution structure can be obtained by determining the connected region according to the hepatic vein projection image, so that the obtained three-dimensional hepatic vein distribution structure can be directly projected on the yz plane to obtain a first region, a second region and a third region, and as shown in fig. 7, for example, a is the first region, B is the second region and C is the third region.

Step 308e, determining a first split line based on the first region, the second region, and the third region.

In the embodiment of the application, the pixel points in the first area, the second area and the third area are fitted, and the corresponding dividing lines are determined.

In other embodiments of the present application, step 308e may be implemented by the following steps a 11-a 13:

and a step a11 of performing linear fitting on the coordinate position of each pixel point in the first area to obtain a first sub-dividing line in the first dividing line.

In the embodiment of the application, the method for performing the line fitting may include a line fitting method such as a least square method.

And a12, performing linear fitting on the coordinate position of each pixel point in the second region to obtain a second sub-dividing line in the first dividing line.

And a step a13 of performing linear fitting on the coordinate position of each pixel point in the third region to obtain a third sub-dividing line in the first dividing line.

And 309, dividing the target liver region mask image sequence based on the first dividing line to obtain a first target segment.

In other embodiments of the present application, step 309 may be implemented by the steps of:

And b11, dividing the mask image sequence of the target liver region by adopting a first sub-dividing line, a second sub-dividing line and a third sub-dividing line, and sequentially determining the divided regions into a left outer region image sequence, a left inner region image sequence, a right front region image sequence and a right rear region image sequence of the liver to be segmented according to a preset coordinate direction.

Wherein the first target segment comprises a left outer region image sequence, a left inner region image sequence, a right front region image sequence, and a right rear region image sequence.

In the embodiment of the application, the preset coordinate direction is determined according to the actual left liver and right liver positions of the liver. The method comprises the steps of dividing each image in a target liver region image sequence by a first sub-dividing line, dividing each image into two parts, dividing the part image of the second sub-dividing line by a second sub-dividing line, dividing the part image of the second sub-dividing line into two parts, dividing the part image of the third sub-dividing line by a third sub-dividing line, dividing each image in the target liver region image sequence into four parts, and sequentially marking the four parts divided by each image in the target liver region image sequence according to the left liver position and the right liver position, so as to obtain a left outer region image sequence, a left inner region image sequence, a right front region image sequence and a right rear region image sequence of each image in the target liver region image sequence, and finally obtaining a left outer region image sequence, a left inner region image sequence, a right front region image sequence and a right rear region image sequence of a liver to be segmented. Exemplary, as shown in fig. 8, the first target segment schematic after division includes a D1 left outer region image sequence, a D2 left inner region image sequence, a D3 right front region image sequence, and a D4 right rear region image sequence, where the E1 first sub-dividing line, the E2 second sub-dividing line, and the E3 third sub-dividing line.

Step 310, segmenting the portal vein image sequence to obtain a target portal vein segment.

In other embodiments of the present application, step 310 may be implemented by the following steps c 11-c 13:

and c11, constructing a portal vein tree based on the portal vein image sequence.

In the embodiment of the application, the portal vein image sequence can be processed by utilizing depth search to construct a portal vein vessel tree. In the process of constructing the portal vein tree by deep search, the portal vein branches after entering the liver from the lower end of the liver, so that a communication area which is positioned at the lower end of the liver and is formed by only one image sequence in the z direction is arranged at the lower end of the liver, and therefore the portal vein area can be determined, namely the portal vein area is used as a root node of the portal vein, and then the corresponding portal vein tree can be obtained based on the root node.

And c12, determining three or more branches in the portal vein tree as target branches.

And c13, analyzing and processing the target branch, and determining an upper and lower image sequence of a left hepatic portal vein of the liver to be segmented and an upper and lower image sequence of a right hepatic portal vein of the liver to be segmented in the portal vein image sequence.

In the embodiment of the application, firstly, classifying treatment is carried out on target branches, for example, the target branches are divided into two groups by adopting a clustering method and the like according to the position of a y axis where the branches are positioned, so as to obtain a first target branch and a second target branch, wherein the first target branch belongs to a left liver, the second target branch belongs to a right liver, and then the first target branch and the second target branch are respectively divided into an upper branch and a lower branch by adopting a clustering method and the like according to the position of the first target branch and the second target branch in a z axis, so that an upper image sequence and a lower image sequence of a left hepatic portal vein of a liver to be segmented and an upper image sequence and a lower image sequence of a right hepatic portal vein of the liver to be segmented are obtained.

Step 311, based on the target portal vein segment, segmenting the first target segment to obtain a target liver segment.

In other embodiments of the present application, step 311 may be implemented by the following steps d11 to d 12:

and d11, dividing the left outer region image sequence based on the upper and lower left hepatic portal vein image sequences to obtain the left outer region upper region image sequence and the left outer region lower region image sequence in the target liver segment.

In other embodiments of the present application, step d11 may be implemented by the following steps:

step d111, acquiring a first coordinate position of each pixel of each image in the left outer region image sequence.

Step d112, obtaining a second coordinate position of each pixel of each image in the upper segment image sequence of the left hepatic portal vein.

Wherein, the image in the left outer region image sequence and the image in the left hepatic portal vein upper segment image sequence have a first corresponding relationship.

In the embodiment of the present application, the first correspondence refers to that since the left hepatic portal vein in the left hepatic portal vein upper segment image sequence is located in the liver in the image in the left outer region image sequence, the icon sequence number in the left hepatic portal vein upper segment image sequence is a part of the image in the left outer region image sequence, and there is a one-to-one correspondence.

Step d113, obtaining a third coordinate position of each pixel of each image in the left hepatic portal subimage sequence.

Wherein the images in the left outer region image sequence and the images in the left portal vein hypomere image sequence have a second correspondence.

In the embodiment of the application, the second correspondence refers to that the left hepatic portal vein in the left hepatic portal vein hyposegment image sequence is located in the liver in the image in the left external region image sequence, so that the icon sequence number in the left hepatic portal vein hyposegment image sequence is a part of the image in the left external region image sequence, and there is a one-to-one correspondence.

Step d114, calculating the distance between the first coordinate position and the second coordinate position based on the first corresponding relation to obtain a first distance set.

In the embodiment of the application, a first correspondence, namely, a distance calculation mode is adopted to calculate a first coordinate position of each pixel of an image with an image sequence number K in the left outer region image sequence and a second coordinate position of each pixel of an image with an image sequence number K in the left hepatic portal vein upper segment image sequence, so as to obtain a distance set of all pixel points of the image with the image sequence number K. The distance calculation mode comprises a Euclidean distance calculation formula, a Chebyshev distance formula and the like.

Step d115, calculating the distance between the first coordinate position and the third coordinate position based on the second corresponding relation to obtain a second distance set.

And d116, classifying the coordinate position of each pixel of each image in the left outer region image sequence by adopting a preset classification algorithm for the first distance set and the second distance set to obtain an upper left outer region image sequence and a lower left outer region image sequence.

In an embodiment of the present application, the preset classification algorithm may include a nearest neighbor method.

And d12, sequentially dividing the right anterior region image sequence and the right posterior region image sequence based on the upper and lower right hepatic portal vein image sequences to obtain a right anterior region upper segment image sequence, a right anterior region lower segment image sequence, a right posterior region upper segment image sequence and a right posterior region lower segment image sequence in the target liver segment.

Wherein the target liver segment further comprises a left inner region image sequence.

In other embodiments of the present application, as shown in fig. 9, after the electronic device performs step 311, the following steps may be further performed:

step 312, marking the liver tail leaf image sequence as 1 segment.

Step 313, marking the left outer region upper segment image sequence as 2 segments, the left outer region lower segment image sequence as 3 segments, the left inner region image sequence as 4 segments, the right front region upper segment image sequence as 5 segments, the right rear region upper segment image sequence as 6 segments, the right rear region lower segment image sequence as 7 segments and the right front region lower segment image sequence as 8 segments.

In other embodiments of the present application, a three-dimensional construction manner may be further adopted to perform three-dimensional construction on the segmented 8-segment liver segmented region, so as to obtain a corresponding segmented model and display the segmented model in a corresponding display region.

Based on the foregoing embodiments, an embodiment of the present application provides an electronic device, which may be applied to the liver image recognition method provided in the embodiments corresponding to fig. 1 to 2, 6 and 9, and referring to fig. 10, the electronic device 4 may include: a processor 41, a memory 42 and a communication bus 43, wherein:

A communication bus 43 for enabling a communication connection between the processor 41 and the memory 42;

a processor 41 for executing a liver image recognition program stored in a memory 42 to realize the steps of:

dividing the medical image sequence, the liver region mask image sequence and the liver vessel mask image sequence by adopting a target classifier to determine a liver tail leaf image sequence;

Removing a liver tail leaf image sequence from the liver region mask image sequence to obtain a target liver region mask image sequence;

And dividing the mask image sequence of the target liver region based on the hepatic vein image sequence and the portal vein image sequence to obtain a target liver segment.

In other embodiments of the present application, the processor performs determining a portal vein image sequence and a hepatic vein image sequence based on the liver region mask image sequence and the hepatic vessel mask image sequence to implement the steps of:

And carrying out refinement treatment on blood vessels in the first blood vessel mask image sequence, and determining a portal vein image sequence and a hepatic vein image sequence.

In other embodiments of the present application, the processor performs refinement processing on the blood vessels in the first blood vessel mask image sequence, and determines a portal vein image sequence and a hepatic vein image sequence, so as to implement the following steps:

Acquiring a blood vessel image sequence in a first position range from the second blood vessel mask image sequence to obtain a portal vein image sequence;

and acquiring a blood vessel image sequence in a second position range from the second blood vessel mask image sequence to obtain a hepatic vein image sequence.

In other embodiments of the present application, the processor performs segmentation of the target liver region mask image sequence based on the hepatic vein image sequence and the portal vein image sequence to obtain a target liver segment, so as to implement the following steps:

dividing the mask image sequence of the target liver region based on a first dividing line to obtain a first target segment;

Segmenting a portal vein image sequence to obtain a target portal vein segment;

based on the target portal vein segment, the first target segment is segmented to obtain a target liver segment.

In other embodiments of the present application, the processor performs determining a first segmentation line for segmenting the hepatic vein image sequence based on the hepatic vein image sequence to implement the steps of:

Determining a first region where a hepatic left vein branch corresponding to a hepatic left vein communication region is located, a second region where a hepatic middle vein branch corresponding to a hepatic middle vein communication region is located and a third region where a hepatic right vein branch corresponding to a hepatic right vein communication region is located in a hepatic vein projection image;

A first split line is determined based on the first region, the second region, and the third region.

In other embodiments of the present application, the processor performs determining the first split line based on the first region, the second region, and the third region to implement the steps of:

performing linear fitting on the coordinate position of each pixel point in the first area to obtain a first sub-dividing line in the first dividing line;

In other embodiments of the present application, the processor performs segmentation of the target liver region mask image sequence based on a first segmentation line to obtain a first target segment, so as to implement the following steps:

Dividing the mask image sequence of the target liver region by adopting a first sub-dividing line, a second sub-dividing line and a third sub-dividing line, and sequentially determining the divided regions into a left outer region image sequence, a left inner region image sequence, a right front region image sequence and a right rear region image sequence of the liver to be segmented according to a preset coordinate direction; wherein the first target segment comprises a left outer region image sequence, a left inner region image sequence, a right front region image sequence, and a right rear region image sequence.

In other embodiments of the present application, the processor performs segmentation of the portal vein image sequence to obtain a target portal vein segment, so as to implement the following steps:

Constructing a portal vein vessel tree based on the portal vein image sequence;

Determining three or more branches in the portal vein tree as target branches;

And analyzing and processing the target branch, and determining an upper and lower left hepatic portal vein image sequence of the liver to be segmented and an upper and lower right hepatic portal vein image sequence of the liver to be segmented in the portal vein image sequence.

In other embodiments of the present application, the processor performs segmentation of the first target segment based on the target portal vein segment to obtain a target liver segment to implement the steps of:

Dividing the right anterior region image sequence and the right posterior region image sequence in sequence based on the upper and lower right hepatic portal vein image sequences to obtain a right anterior region upper segment image sequence, a right anterior region lower segment image sequence, a right posterior region upper segment image sequence and a right posterior region lower segment image sequence in the target liver segment; wherein the target liver segment further comprises a left inner region image sequence.

In other embodiments of the present application, the processor performs segmentation of the left outer region image sequence based on the left hepatic portal vein upper and lower segment image sequences to obtain the left outer region upper segment image sequence and the left outer region lower segment image sequence in the target liver segment, so as to implement the following steps:

acquiring a third coordinate position of each pixel of each image in the left portal vein hypomere image sequence; the images in the left outer region image sequence and the images in the left portal vein hypomere image sequence have a second corresponding relation;

In other embodiments of the present application, the processor performs segmentation of the liver region mask image sequence based on the hepatic vein image sequence and the portal vein image sequence after removing the hepatic tail leaf based on the hepatic tail leaf image sequence, to obtain a target liver segment, and is further configured to perform the following steps:

Marking the tail leaf image sequence of the liver as 1 segment;

The left outer region upper section image sequence is marked as 2 sections, the left outer region lower section image sequence is marked as 3 sections, the left inner region image sequence is marked as 4 sections, the right front region upper section image sequence is marked as 5 sections, the right rear region upper section image sequence is marked as 6 sections, the right rear region lower section image sequence is marked as 7 sections and the right front region lower section image sequence is marked as 8 sections.

It should be noted that, in the specific implementation process of the steps executed by the processor in this embodiment, reference may be made to the implementation process in the liver image recognition method provided in the embodiments corresponding to fig. 1 to 2, 6 and 9, which is not described herein again.

The embodiment of the application provides electronic equipment, which is used for acquiring a medical image sequence of a liver to be segmented, determining a corresponding liver region mask image sequence and a liver blood vessel mask image sequence based on the medical image sequence, determining a portal vein image sequence and a hepatic vein image sequence based on the liver region mask image sequence and further the liver blood vessel mask image sequence, determining a portal vein image sequence and a hepatic vein image sequence based on the liver region mask image sequence and the liver blood vessel mask image sequence, segmenting the medical image sequence, the liver region mask image sequence and the liver blood vessel mask image sequence by using a target classifier, determining a liver tail-like leaf image sequence, removing the liver tail-like leaf image sequence from the liver region mask image sequence to obtain a target liver region mask image sequence, and finally segmenting the target liver region mask image sequence based on the hepatic vein image sequence and the portal vein image sequence to obtain a target liver segment. In this way, the medical image sequence, the liver region mask image sequence and the liver blood vessel mask image sequence are segmented, after the liver tail-shaped leaf image sequence is determined, the target liver region mask image sequence of the liver region mask image sequence after the liver tail-shaped leaf image sequence is removed, and the target liver region mask image sequence is segmented through the portal vein image sequence and the liver vein image sequence to obtain a target segment, so that the segmentation of the liver tail-shaped leaf image sequence is realized, the segmentation of the target liver region mask image sequence based on the portal vein image sequence and the liver vein image sequence is realized, the problem that the liver part cannot be segmented fully automatically in the prior art is solved, and the accuracy of segmenting the liver part by the electronic equipment and the intelligent degree of the electronic equipment are improved.

Based on the foregoing embodiments, embodiments of the present application provide a computer readable storage medium storing one or more programs, where the one or more programs may be executed by one or more processors to implement the implementation procedure of the liver image recognition method provided in the embodiments corresponding to fig. 1 to 2, 6, and 9, which are not described herein.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the present application.

Claims

1. A liver image recognition method, the method comprising:

Carrying out refinement treatment on blood vessels in the first blood vessel mask image sequence, and determining a portal vein image sequence and a hepatic vein image sequence;

2. The method of claim 1, wherein the refining of the blood vessels in the first sequence of vessel mask images to determine a sequence of portal images and a sequence of hepatic images, comprises:

3. The method of claim 1, the segmenting the target liver region mask image sequence based on the hepatic vein image sequence and the portal vein image sequence to obtain a target liver segment, comprising:

4. A method according to claim 3, the determining a first segmentation line for segmenting the hepatic vein image sequence based on the hepatic vein image sequence, comprising:

5. The method of claim 4, the determining the first split line based on the first region, the second region, and the third region comprising:

6. The method of claim 5, the segmenting the target liver region mask image sequence based on the first segmentation line to obtain a first target segment, comprising:

7. The method of claim 6, the segmenting the sequence of portal images to obtain a target portal segment, comprising:

Constructing a portal vein tree based on the portal vein image sequence;

8. The method of claim 7, the segmenting the first target segment based on the target portal vein segment resulting in the target liver segment, comprising:

9. The method according to claim 8, wherein the segmenting the left outer region image sequence based on the left hepatic portal vein upper and lower segment image sequences to obtain a left outer region upper segment image sequence and a left outer region lower segment image sequence in the target liver segment comprises:

10. The method of claim 9, wherein the segmenting the liver region mask image sequence based on the hepatic vein image sequence and the portal vein image sequence after removing the hepatic tail based on the hepatic tail image sequence, to obtain the target liver segment, further comprises:

Marking the liver tail leaf image sequence as 1 segment;

11. An electronic device, the electronic device comprising: a processor, a memory, and a communication bus, wherein:

the memory is used for storing executable instructions;

the processor is configured to execute a liver image recognition program stored in the memory, and implement the liver image recognition method according to any one of claims 1 to 10.

12. A storage medium having stored thereon a liver image recognition program which, when executed by a processor, implements the steps of the liver image recognition method of any one of claims 1 to 10.